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philadelphiabuildings

Projects – 216,978. Architects – 20,653. Images – 84,293. Institutions - 31. Hits – 42,000 a day. Hits since 2000 – over 11 million. www.philadelphiabuildings.org. All Philadelphia Register Buildings. All PA State Inventory Buildings. All National Register Buildings.

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philadelphiabuildings

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  1. Projects – 216,978 Architects – 20,653 Images – 84,293 Institutions - 31 Hits – 42,000 a day Hits since 2000 – over 11 million www.philadelphiabuildings.org

  2. All Philadelphia Register Buildings All PA State Inventory Buildings All National Register Buildings All HABS-PABuildings Geographical Coverage All 50 States 57 Countries www.philadelphiabuildings.org

  3. Baxter’s Panoramic Business Directory, 1859

  4. Rae’s Philadelphia Pictorial Directory & Panoramic Advertiser, 1851

  5. D.J. Kennedy Watercolor, Historical Society of Pennsylvania, K.1-40.1

  6. Green’s Hotel, from Philadelphia Pa., the Book of its Bourse, 1898, p. 67

  7. New Masonic Hall, Wainwright Lithograph Collection, Library Company of Philadelphia

  8. Philadelphia Contributionship fire insurance survey, number 9521

  9. McElroy Directory, 1857

  10. Optical Character Recognition Process

  11. Spreadsheet made from OCR Text

  12. Hexamer & Locher Atlas, 1858, Free Library of Philadelphia

  13. 702 Chestnut Street Archival Tiff Format with Color Control, Caption Information and Scale

  14. Detail of 1858 Hexamer & Locher, illustrating georeferencing with current Philadelphia street centerlines

  15. Centralized Model Athenaeum (PAB) CML, US Census, research projects Arch. Drawings Demo- graphics Central Data and App Server(s) Photos GIS Maps Scanned Maps Athenaeum, Library Company, HSP, Phila. Dept. of Records Phila. Dept. of Records, City Planning, Water Dept. Athenaeum, Free Library

  16. Centralized Model • Data stored in one place • Does not meet local institutional requirements (thus duplicated systems) • Difficult to plan and scale • Difficult to agree on standards: • Metadata • Data refreshing • Access control, use agreements

  17. Centralized Model • Applications run on centralized servers • Faster, but must be controlled in one place

  18. Distributed Model Scanned Maps GIS Maps PACSCL Dept. of Records Photos Land Use Photos Phila. Negro Photo Archives Historical Context PAB CML Applications Data Arch. Drawings Demo- graphics Repositories

  19. Distributed Model • Data stored by each institution (or small group of institutions) • or in a distributed set of centralized systems • Can be built using existing systems, or systems designed for other purposes • Scales by institution • Need to agree on only basic standards: • Metadata (Dublin Core + Geographic Info) • Protocols for data harvesting

  20. Distributed Model • Applications run from anywhere • Applications can be built as needed for particular projects and by different groups • Slower (retrieving data from different sources takes time) • But needed data can be cached (like Google) to provide faster responses

  21. Data Distribution Approaches • Entirely map-based, GIS systems • Open Geospatial Consortium (OGC) Web Map Service (WMS) • Proprietary options (SDEs, ESRI, Google Earth) • Entirely data-based systems • Open Archives Initiative (OAI)Protocol for Metadata Harvesting (PMH) • XML gateways, Z39.50, and the rest

  22. Map-based systems Easier application development Wider compatibility with existing map client software Significant entry barriers (cost/server speed) for providers Data-based systems Application/network provides geocoding Wide support by existing data server software Lower entry barriers for data providers Data Distribution Approaches

  23. The Solution? • Mixed Network Models • Distributed resources, with centralized systems where appropriate • Mixed Data Distribution Approaches • For image-rich base maps (atlas plates, aerial photography, etc.) – WMS • For data-rich collections (historical photos, city directories) – OAI + spatial + temporal.

  24. Challenges • Metadata Standards – Dublin Core? • Geospatial and Temporal Metadata • Map extents, feature points • Date and time, both instant and spanned • Precision, Specificity, and Uncertainty • “702 Chestnut St.” vs. “7th & Chestnut” vs. “Chestnut St.” vs. “Philadelphia” • “1902” vs. “c. 1900” vs. “Early 20th Century”

  25. Challenges (cont’d) • Aggregation / Correlation / Change • Changing place names, boundaries, addresses, and uncertain correlations (city directories) • Re-aggregating data sets (e.g., census data) to provide useful comparisons over time • User Interface • Providing useful interaction both to experience GIS experts, but also to scholars and hobbyists, and even tourists.

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